Significance: In line with the development and integration of new power systems, many large-scale renewable energy bases-particularly wind and photovoltaic-are being rapidly established in desert, Gobi, and other arid (DGA) regions across China and beyond. These regions are characterized by harsh climatic and geological conditions, making the reliable operation and rapid recovery of electrical infrastructure under extreme weather events increasingly critical. The increasing frequency and intensity of extreme weather events under global climate change further amplify this challenge. This study investigates the safe operation and resilience of large-scale renewable energy bases in DGA regions under extreme environmental conditions. This study aims to systematically review the impact of various extreme hazards on electrical equipment across the power system chain, assess the current state of disaster prevention and mitigation technologies, and identify critical technical needs for future development. This study provides a solid theoretical foundation and practical technological support for enhancing the resilience and intelligent transformation of modern power systems. Progress: A comprehensive review was conducted on recent domestic and international cases of power system failure and associated economic losses triggered by extreme weather events, including extreme low and high ambient temperatures, atmospheric icing, strong winds, sand and dust storms, earthquakes, lightning strikes, wildfires, floods, and secondary compound disasters. The analysis covers the full lifecycle of electrical infrastructure, including the power generation, transmission, and transformation stages. For each stage, critical threats to the operational security and structural integrity of key electrical equipment are identified. The results indicate that the unique environmental characteristics of DGA regions-high solar radiation, strong convective winds, large diurnal temperature variations, and frequent sandstorms-exacerbate the vulnerability of electrical equipment, particularly outdoor components such as transformers, insulators, switchgears, and towers. The primary types and impact mechanisms of extreme environmental factors on equipment in DGA regions are categorized. Their associated degradation modes, including material embrittlement due to low temperatures, overheating and insulation aging under extreme heat, salt fog and corrosion effects, mechanical fatigue from wind-induced vibration, and flashover risks due to pollution and icing, are discussed in detail. This study delineates the specific vulnerabilities of various types of electrical equipment and the main failure modes associated with each hazard. The current status of monitoring, early warning, emergency response, and disaster mitigation technologies is also critically analyzed. Although solutions such as online monitoring systems, structural reinforcement methods, seismic isolation devices, de-icing systems, and vibration-damping technologies have been proposed and partially implemented, many challenges remain. Despite promising results from pilot-scale deployments and demonstration projects, large-scale practical applications are hindered by technical bottlenecks. These include insufficient monitoring precision in complex environments, limited capacity for real-time online condition assessment, and reduced effectiveness in multihazard detection and degradation tracking. Furthermore, challenges in data integration, system interoperability, and long-term stability in harsh environments significantly undermine the reliability of disaster response systems in real-world engineering applications. Conclusions and Prospects: As the risks posed by extreme climate events continue to grow, transitioning from passive disaster response to active, intelligent risk management across the entire lifecycle of power systems is urgently needed. Future efforts should focus on creating a standardized, modular framework for disaster prevention and mitigation that can be rapidly adapted to a wide range of hazards. Intelligent decision-making platforms, supported by digital twin models, big data analytics, and AI-driven prediction algorithms, should be developed to provide real-time operational guidance under extreme conditions. Moreover, cross-disciplinary collaboration among meteorology, materials science, structural engineering, and electrical engineering is essential for designing equipment and systems inherently resistant to compound disasters.